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Agent skill

AI Inference Service Mesh

by sickn33 in sickn33/agentic-awesome-skills

Use service mesh patterns for AI inference traffic management, mTLS, canary releases, policy enforcement, and cross-cluster resilience.

MITAuto-check passedBackend & APIs

Install AI Inference Service Mesh

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill ai-inference-service-mesh -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills ai-inference-service-mesh --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai-inference-service-mesh .claude/skills/ai-inference-service-mesh && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
ai-inference-service-mesh
GitHub stars
47k
Used in
2 other repos
Token cost
~2.7k tokens
SKILL.md length
242 words
Files
1
Skills in repo
1,497
Repo updated
First seen
Licence
MIT

At a glance

Use service mesh patterns for AI inference traffic management, mTLS, canary releases, policy enforcement, and cross-cluster resilience.

  • Tasks that involve Microservices
  • SKILL.md covers Why Mesh for AI, Prerequisites, Core Patterns and Traffic Management, plus 6 more sections
  • Calls kubectl, curl and jq
  • Tasks that involve Deployment

What it does

AI Inference Service Mesh is an agent skill from sickn33/agentic-awesome-skills. Use service mesh patterns for AI inference traffic management, mTLS, canary releases, policy enforcement, and cross-cluster resilience.

Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: Requires the relevant OS/platform tooling and privileged access where noted. Docs-only; helper scripts and templates not bundled.

It sits in Backend & APIs, covering Microservices and Deployment. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

When your agent uses it

  • Tasks that involve Microservices
  • Tasks that involve Deployment

Example prompts

  • “/ai-inference-service-mesh”

Requirements

  • Compatibility (from SKILL.md): Requires the relevant OS/platform tooling and privileged access where noted. Docs-only; helper scripts and templates not bundled.

What it can do on your machine

Read from SKILL.md and the folder at commit b84d35a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • kubectl
    • curl
    • jq

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use kubectl and curl, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Requires the relevant OS/platform tooling and privileged access where noted. Docs-only; helper scripts and templates not bundled.

    From compatibility in the SKILL.md frontmatter.

Context cost

AI Inference Service Mesh loads about 2.7k tokens when it runs. Until then it costs about 40 tokens; SKILL.md has 242 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~40
When it runs · the whole SKILL.md, loaded when a task matches
~2.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sickn33/agentic-awesome-skills at commit b84d35a, republished under its MIT licence (© sickn33). 242 words, ~2,670 tokens.

Download SKILL.mdSave it as .claude/skills/ai-inference-service-mesh/SKILL.md (or your agent's skills folder).
name
ai-inference-service-mesh
description
Use service mesh patterns for AI inference traffic management, mTLS, canary releases, policy enforcement, and cross-cluster resilience.
compatibility
Requires the relevant OS/platform tooling and privileged access where noted. Docs-only; helper scripts and templates not bundled.
category
devops
risk
critical
source
https://github.com/BagelHole/DevOps-Security-Agent-Skills
source_repo
BagelHole/DevOps-Security-Agent-Skills
source_type
community
date_added
2026-09-20
license
MIT
license_source
https://github.com/BagelHole/DevOps-Security-Agent-Skills/blob/main/LICENSE
metadata.author
devops-skills
metadata.version
1.0

AI Inference Service Mesh

Apply Istio/Linkerd mesh controls to secure and optimize east-west AI traffic across inference microservices.

Why Mesh for AI

  • Enforce mTLS between gateway, retriever, reranker, and model services
  • Apply fine-grained traffic policies without app code changes
  • Run progressive delivery for model-serving backends
  • Observe latency hops for retrieval + generation chains
  • Route inference requests by model version, tenant, or priority tier
  • Protect expensive GPU-backed services from cascading failures

Prerequisites

bash
# Install Istio with production profile
istioctl install --set profile=default \
  --set meshConfig.accessLogFile=/dev/stdout \
  --set meshConfig.defaultConfig.holdApplicationUntilProxyStarts=true

# Label inference namespace for sidecar injection
kubectl create namespace ai-inference
kubectl label namespace ai-inference istio-injection=enabled

# Verify installation
istioctl verify-install
istioctl analyze -n ai-inference

Core Patterns

mTLS Strict Mode Cluster-Wide
yaml
apiVersion: security.istio.io/v1beta1
kind: PeerAuthentication
metadata:
  name: default
  namespace: istio-system
spec:
  mtls:
    mode: STRICT
---
# Namespace-level override if needed for gradual rollout
apiVersion: security.istio.io/v1beta1
kind: PeerAuthentication
metadata:
  name: ai-inference-mtls
  namespace: ai-inference
spec:
  mtls:
    mode: STRICT
  portLevelMtls:
    # gRPC inference port
    8081:
      mode: STRICT
    # Prometheus metrics port - allow plaintext scraping
    9090:
      mode: PERMISSIVE
AuthorizationPolicy Per Service Account
yaml
apiVersion: security.istio.io/v1beta1
kind: AuthorizationPolicy
metadata:
  name: model-server-access
  namespace: ai-inference
spec:
  selector:
    matchLabels:
      app: model-server
  action: ALLOW
  rules:
  - from:
    - source:
        principals:
        - "cluster.local/ns/ai-inference/sa/api-gateway"
        - "cluster.local/ns/ai-inference/sa/orchestrator"
    to:
    - operation:
        methods: ["POST"]
        paths: ["/v1/predict", "/v1/embeddings", "/v2/models/*/infer"]
---
apiVersion: security.istio.io/v1beta1
kind: AuthorizationPolicy
metadata:
  name: deny-external-to-retriever
  namespace: ai-inference
spec:
  selector:
    matchLabels:
      app: vector-retriever
  action: DENY
  rules:
  - from:
    - source:
        notNamespaces: ["ai-inference"]
Egress Policy for Approved Model Endpoints
yaml
apiVersion: networking.istio.io/v1alpha3
kind: ServiceEntry
metadata:
  name: openai-api
  namespace: ai-inference
spec:
  hosts:
  - api.openai.com
  ports:
  - number: 443
    name: https
    protocol: TLS
  resolution: DNS
  location: MESH_EXTERNAL
---
apiVersion: networking.istio.io/v1alpha3
kind: DestinationRule
metadata:
  name: openai-api-tls
  namespace: ai-inference
spec:
  host: api.openai.com
  trafficPolicy:
    tls:
      mode: SIMPLE
    connectionPool:
      http:
        h2UpgradePolicy: UPGRADE
      tcp:
        maxConnections: 50
---
apiVersion: security.istio.io/v1beta1
kind: AuthorizationPolicy
metadata:
  name: restrict-egress
  namespace: ai-inference
spec:
  action: ALLOW
  rules:
  - to:
    - operation:
        hosts:
        - "api.openai.com"
        - "models.anthropic.com"
        - "*.blob.core.windows.net"

Traffic Management

VirtualService for A/B Model Testing
yaml
apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
metadata:
  name: model-server
  namespace: ai-inference
spec:
  hosts:
  - model-server
  http:
  # Route by header for explicit model version selection
  - match:
    - headers:
        x-model-version:
          exact: "v2-experimental"
    route:
    - destination:
        host: model-server
        subset: v2-experimental
    timeout: 120s
  # Route by header for A/B test cohort
  - match:
    - headers:
        x-ab-cohort:
          exact: "treatment"
    route:
    - destination:
        host: model-server
        subset: v2-experimental
      weight: 100
    timeout: 120s
  # Default traffic split: 90/10 canary
  - route:
    - destination:
        host: model-server
        subset: v1-stable
      weight: 90
    - destination:
        host: model-server
        subset: v2-experimental
      weight: 10
    timeout: 60s
    retries:
      attempts: 2
      perTryTimeout: 30s
      retryOn: unavailable,resource-exhausted
DestinationRule with Subsets
yaml
apiVersion: networking.istio.io/v1alpha3
kind: DestinationRule
metadata:
  name: model-server
  namespace: ai-inference
spec:
  host: model-server
  trafficPolicy:
    connectionPool:
      http:
        h2UpgradePolicy: UPGRADE
        maxRequestsPerConnection: 100
      tcp:
        maxConnections: 200
        connectTimeout: 5s
    loadBalancer:
      simple: LEAST_REQUEST
  subsets:
  - name: v1-stable
    labels:
      version: v1
    trafficPolicy:
      connectionPool:
        http:
          maxRequestsPerConnection: 50
  - name: v2-experimental
    labels:
      version: v2
    trafficPolicy:
      connectionPool:
        http:
          maxRequestsPerConnection: 20
Circuit Breaking for Inference Backends
yaml
apiVersion: networking.istio.io/v1alpha3
kind: DestinationRule
metadata:
  name: model-server-circuit-breaker
  namespace: ai-inference
spec:
  host: model-server
  trafficPolicy:
    connectionPool:
      tcp:
        maxConnections: 100
        connectTimeout: 10s
      http:
        http1MaxPendingRequests: 50
        http2MaxRequests: 200
        maxRequestsPerConnection: 10
        maxRetries: 3
    outlierDetection:
      consecutive5xxErrors: 3
      interval: 15s
      baseEjectionTime: 30s
      maxEjectionPercent: 50
      minHealthPercent: 30
      splitExternalLocalOriginErrors: true
---
# Separate circuit breaker for the vector retriever
apiVersion: networking.istio.io/v1alpha3
kind: DestinationRule
metadata:
  name: vector-retriever-circuit-breaker
  namespace: ai-inference
spec:
  host: vector-retriever
  trafficPolicy:
    connectionPool:
      tcp:
        maxConnections: 300
      http:
        http1MaxPendingRequests: 200
        http2MaxRequests: 500
    outlierDetection:
      consecutive5xxErrors: 5
      interval: 10s
      baseEjectionTime: 15s
      maxEjectionPercent: 30
Retry Budget for Streaming Requests
yaml
apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
metadata:
  name: streaming-inference
  namespace: ai-inference
spec:
  hosts:
  - model-server
  http:
  # Streaming endpoint: no retries, long timeout
  - match:
    - uri:
        prefix: /v1/stream
    route:
    - destination:
        host: model-server
        subset: v1-stable
    timeout: 300s
    retries:
      attempts: 0
  # Embeddings endpoint: safe to retry, short timeout
  - match:
    - uri:
        prefix: /v1/embeddings
    route:
    - destination:
        host: model-server
        subset: v1-stable
    timeout: 15s
    retries:
      attempts: 3
      perTryTimeout: 5s
      retryOn: 5xx,reset,connect-failure,retriable-status-codes

Resilience

Locality-Aware Routing
yaml
apiVersion: networking.istio.io/v1alpha3
kind: DestinationRule
metadata:
  name: model-server-locality
  namespace: ai-inference
spec:
  host: model-server
  trafficPolicy:
    loadBalancer:
      localityLbSetting:
        enabled: true
        distribute:
        - from: "us-east-1/us-east-1a/*"
          to:
            "us-east-1/us-east-1a/*": 80
            "us-east-1/us-east-1b/*": 20
        failover:
        - from: us-east-1
          to: us-west-2
    outlierDetection:
      consecutive5xxErrors: 3
      interval: 10s
      baseEjectionTime: 30s

Observability

yaml
# Telemetry resource for custom metrics on inference services
apiVersion: telemetry.istio.io/v1alpha1
kind: Telemetry
metadata:
  name: inference-telemetry
  namespace: ai-inference
spec:
  metrics:
  - providers:
    - name: prometheus
    overrides:
    - match:
        metric: REQUEST_DURATION
        mode: CLIENT_AND_SERVER
      tagOverrides:
        model_name:
          operation: UPSERT
          value: "request.headers['x-model-name']"
        tenant_id:
          operation: UPSERT
          value: "request.headers['x-tenant-id']"
  tracing:
  - providers:
    - name: zipkin
    randomSamplingPercentage: 10.0
Kiali Dashboard Check
bash
# Port-forward Kiali
kubectl port-forward svc/kiali -n istio-system 20001:20001 &

# Verify mesh health via API
curl -s http://localhost:20001/kiali/api/namespaces/ai-inference/health | jq .

# Check proxy sync status
istioctl proxy-status -n ai-inference

# Debug a specific pod sidecar config
istioctl proxy-config routes deploy/model-server -n ai-inference -o json
istioctl proxy-config cluster deploy/model-server -n ai-inference

Pitfalls to Avoid

  • Aggressive timeouts that break streaming responses -- set 300s+ for generation endpoints
  • Blanket retries that amplify expensive generation calls -- disable retries on non-idempotent routes
  • Missing identity boundaries between tenant-facing and internal services
  • Forgetting to exempt health check and metrics ports from strict mTLS
  • Setting outlier ejection too aggressively on small pools (maxEjectionPercent too high)
  • Not using holdApplicationUntilProxyStarts causing race conditions on startup
  • service-mesh (service-mesh) - Foundational mesh concepts
  • llm-gateway (llm-gateway) - North-south API gateway controls
  • opentelemetry (opentelemetry) - End-to-end tracing and metrics

When to Use

  • You are provisioning, configuring, or troubleshooting the infrastructure component covered by this skill (servers, storage, databases, networking, cloud, local AI).

Limitations

  • Infrastructure commands can disrupt services: confirm target host/scope and have backups/snapshots before mutating state.
  • Docs-only import: upstream scripts and templates not bundled.

© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/ai-inference-service-mesh of sickn33/agentic-awesome-skills.

Open the folder on GitHubat commit b84d35a

Used in 2 other repositories

We found 6 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Categories

Questions about AI Inference Service Mesh

What does AI Inference Service Mesh do?

Use service mesh patterns for AI inference traffic management, mTLS, canary releases, policy enforcement, and cross-cluster resilience. AI Inference Service Mesh is an agent skill from sickn33/agentic-awesome-skills. Use service mesh patterns for AI inference traffic management, mTLS, canary releases, policy enforcement, and cross-cluster resilience.

When should I use AI Inference Service Mesh?

AI Inference Service Mesh fits situations like: tasks that involve Microservices; tasks that involve Deployment.

How do I install AI Inference Service Mesh in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill ai-inference-service-mesh -a claude-code`. Or copy the skill folder (skills/ai-inference-service-mesh in sickn33/agentic-awesome-skills) into .claude/skills/ai-inference-service-mesh in your project. Claude Code loads it when a task matches its description.

How do I install AI Inference Service Mesh in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill ai-inference-service-mesh -a codex`. Or copy the skill folder (skills/ai-inference-service-mesh in sickn33/agentic-awesome-skills) into .agents/skills/ai-inference-service-mesh in your project. Codex loads it when a task matches its description.

Can I use AI Inference Service Mesh in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill ai-inference-service-mesh -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ai-inference-service-mesh, .gemini/skills/ai-inference-service-mesh, .github/skills/ai-inference-service-mesh and .opencode/skills/ai-inference-service-mesh in your project.

What does AI Inference Service Mesh need to run?

Going by SKILL.md and its folder, AI Inference Service Mesh needs the command-line tools its instructions call (kubectl, curl and jq). Compatibility (from SKILL.md): Requires the relevant OS/platform tooling and privileged access where noted. Docs-only; helper scripts and templates not bundled..

Does AI Inference Service Mesh access the network?

SKILL.md contains no URLs. Its commands use curl, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is AI Inference Service Mesh safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does AI Inference Service Mesh use?

AI Inference Service Mesh is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does AI Inference Service Mesh use?

About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to AI Inference Service Mesh?

Skills that share tags, products or a category with AI Inference Service Mesh: Rtvi Vlm Perf Testing (NVIDIA-AI-Blueprints/video-search-and-summarization, 1.9k stars), Vss Build Vision AI (NVIDIA-AI-Blueprints/video-search-and-summarization, 1.9k stars), Vss Deploy Profile (NVIDIA/skills, 3.6k stars) and Rtvi Vlm Customize Model (NVIDIA/skills, 3.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains AI Inference Service Mesh?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,405 GitHub stars. The repository holds 1,497 skills in this directory. The repository was last updated on October 9, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.